(555b) Community-Scale Residential Air Conditioning Control for Effective Grid Management

Cole, W., University of Texas at Austin
Gorman, W., University of Texas at Austin
Webber, M. E., UT Austin
Edgar, T. F., McKetta Department of Chemical Engineering, The University of Texas at Austin

As of 2011, there were over 132 million housing units in the United States [1], and of those 87% had air conditioning units [2]. In the southern United States, the percentage of homes with air conditioning approaches 100% [2]. These air conditioning units exacerbate peak electricity demand issues, straining the grid [3]. For example, during the summer peak in 2011 in the Electric Reliability Council of Texas (ERCOT) grid, over 50% of the total electrical load was from residential homes [4], primarily driven by their air conditioning systems. The residential peak summer demand increases by almost 400% from shoulder seasons (e.g., March, October). Because of the wide range in power demand due to air conditioners, the residential sector will be an important contributor in future grid management.

Using energy audits, homeowner survey data, web map images, and actual electricity meter data, we have created and tested 60 unique residential home energy models in EnergyPlus, a building energy simulation tool, for actual homes in Austin, Texas. Each of these home models includes geometries, constructions (e.g., insulation, windows), occupancy schedules, and orientations specific to the home from which it was modeled. Each home’s electricity meter data from 2011-2012 were used to validate the home models, which across all the homes had an average error of 5%.

This work expands our previous work ([5], [6]) that developed an automated model reduction technique for converting an EnergyPlus residential home model into a linear, discrete-time dynamic model. This model uses inputs of weather, thermostat set point, and air conditioning energy consumption to accurately predict air conditioning energy consumption up to 24 hours into the future. The model was used in a model predictive controller (MPC) that minimized energy costs for a single home in the face of real-time electricity prices.

Combining the 60 residential home models with the reduced-order modeling technique allows us to simulate and control a theoretical community and evaluate the demand response potential of that community. We use the 60 residential home models to create a 900-home community, which is represented by 900 unique reduced-order linear dynamic models. This community is built such that it is representative of communities in hot and dry climates such as Austin.

In this analysis, we present the results of using MPC to control residential home air conditioning units (via the homes’ thermostats) in this simulated community in order to mitigate peak demand issues. We consider real-time electricity settlement point (wholesale market) prices as an indicator of grid strain. Centralized and decentralized model predictive control methods are compared, including their effectiveness and computational requirements. We discuss the effects of implementing residential air conditioning control across an entire utility and the impacts it can have on peak demand. Lastly, we consider some simple rules that were learned from the MPC that can be implemented across the different households in order to approach the optimal peak reduction scenario without requiring any control or optimization schemes.


[1]        US Census Bureau, “USA QuickFacts from the US Census Bureau,” USA QuickFacts from the US Census Bureau, 2013. [Online]. Available: http://quickfacts.census.gov/qfd/states/00000.html. [Accessed: 01-Feb-2013].

[2]        B. McNary and C. Berry, “How Americans are using energy today,” presented at the 2012 ACEEE Summer Study on Energy Efficiency in Buildings, Pacific Grove, CA, 2012, vol. 1, pp. 204–215.

[3]        J. D. Rhodes, B. Stephens, and M. E. Webber, “Using energy audits to investigate the impacts of common air-conditioning design and installation issues on peak power demand and energy consumption in Austin, Texas,” Energy Build., vol. 43, no. 11, pp. 3271–3278, Nov. 2011.

[4]        P. Wattles, “ERCOT Demand Response Overview & Status Report,” AMIT-DSWG Workshop “AMI”s Next Frontier: Demand Response’, 30-Aug-2011. [Online]. Available: http://www.ercot.com/content/meetings/dswg/keydocs/2011/0830/3_ERCOT_pre.... [Accessed: 29-Mar-2013].

[5]        W. J. Cole, E. T. Hale, and T. F. Edgar, “Building energy model reduction for model predictive control using OpenStudio,” in Proceedings of the 2013 American Control Conference, Washington, D.C., 2013.

[6]        W. J. Cole, E. T. Hale, and T. F. Edgar, “Reduced-order residential home modeling for model predictive control,” Energy Build., under review 2013.


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